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4 Data Management Mistakes to Avoid

As applications for big data continue to grow rapidly, an increasing number of businesses are opting for digital transformation so that they can maintain relevance while keeping themselves on top of the latest trends. However, while most organizations recognize data’s importance and treat them as assets due to their ability to determine growth trajectory and offer them a competitive advantage, there are still those who fail to draw valuable insights from them.

In recent years, businesses worldwide have ramped their governance efforts up with enterprise data, but few have managed to succeed. The reality is that many struggle with the overwhelming data that needs to be understood and controlled to remain competitive and meet consumer needs because of easily avoidable mistakes. With that said, here are some data management pitfalls to avoid.

  • Data governance absence

Before implementation, there needs to be a reliable governance framework for data to ensure that the life cycle remains in check. This is only possible when governing authorities composed of skilled people who can adequately oversee data administration are formed. In the event of a problem, this body should possess the authority to search for answers from those who are a part of the project for data governance. This shouldn’t be taken for granted because it will give better control over the process.

  • Taking data quality for granted

Data accuracy and consistency drive success, whether or not you agree with it. With a reliable data management platform from Dataloop and sufficient measures in place, you can ensure data integrity. Decisions made for the business will always be data-driven, and if it’s of questionable quality, they’re likely to produce poor results. In other words, you need to have reliable and correct data from which to get insights and ensure only informed decisions will be made. When quality is maintained, the standardized evaluation of data will be a reality.

  • Using a silo approach on data governance

Although programs in data governance that are limited to specific business units might help, issues may arise because data sharing generally happens with various business groups that have their definitions for particular elements of data. As a result, this can potentially result in poor decisions. Therefore, to ensure the success of data governance, organizations need to treat data like a business asset because it is.

  • Collecting unnecessary data

One of data governance’s key features is a retirement strategy for data. At specific points, all data will have to be recycled. However, organizations that don’t do so will have to add extra cycles to ensure that everything remains in order and that redundant, trivial, and obsolete data are removed.


When going on the data management journey, you need to look at it as an ongoing process and start small while aiming big. So instead of going all out and making yourself more susceptible to the mistakes listed above, try to keep your focus on one or two areas at a time. Likewise, don’t aim to achieve all your goals and address every concern during the initial stages of the process. In this way, you’ll avoid being overwhelmed and making any potentially costly mistakes.


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